Opensky

OpenSky provides api to get data for flights in progress. It is rate limited

Code

import requests
import pandas as pd
import geopandas

Code

def fetch_opensky_data():
    url = "https://opensky-network.org/api/states/all"
    response = requests.get(url)
    data = response.json()

    columns = [
        "icao24", "callsign", "origin_country", "time_position", "last_contact",
        "longitude", "latitude", "baro_altitude", "on_ground", "velocity",
        "heading", "vertical_rate", "sensors", "geo_altitude", "squawk",
        "spi", "position_source"
    ]

    df_raw = pd.DataFrame(data['states'], columns=columns)
    return (
        df_raw
        .assign(
            time_position=pd.to_datetime(df_raw["time_position"], unit="s"),
            last_contact=pd.to_datetime(df_raw["last_contact"], unit="s"),
        )
    )

df_flights = fetch_opensky_data()


df_flihts_non_nan = df_flights.dropna(subset=["longitude", "latitude"])
gdf = geopandas.GeoDataFrame(
    df_flihts_non_nan,
    geometry=geopandas.points_from_xy(df_flihts_non_nan.longitude, df_flihts_non_nan.latitude),
    crs="EPSG:4326",
)

gdf.explore(fullscreen=True, tooltip=False, popup=True)

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Flights in world at notebook time

Number of flights by origin_country

Code

(
    df_flights
    .groupby("origin_country")
    .agg(num_flights=("icao24", "count"))
    .sort_values("num_flights", ascending=False)
    .head(20)
)

	num_flights
origin_country
United States	1549
Australia	282
Germany	257
Ireland	253
China	245
Turkey	241
India	214
United Kingdom	180
Japan	162
France	158
Malta	155
Spain	150
Canada	146
Austria	142
Kingdom of the Netherlands	114
United Arab Emirates	110
Switzerland	97
Poland	87
Republic of Korea	73
Thailand	65

Top 20 countries with most flights

All flights

For reference

Code

df_flights

	icao24	callsign	origin_country	time_position	last_contact	longitude	latitude	baro_altitude	on_ground	velocity	heading	vertical_rate	sensors	geo_altitude	squawk	spi	position_source
0	39de4f	TVF13AU	France	2025-05-23 05:22:23	2025-05-23 05:22:24	15.2441	43.6133	11574.78	False	224.35	302.45	-0.65	None	11666.22	2053	False	0
1	801636	VTAQL	India	2025-05-23 05:22:25	2025-05-23 05:22:25	72.2426	19.5978	5204.46	False	173.03	138.01	-4.88	None	5394.96	None	False	0
2	39de4b	TVF72TD	France	2025-05-23 05:22:24	2025-05-23 05:22:25	12.1106	52.2264	8686.80	False	187.43	261.16	9.75	None	8496.30	1000	False	0
3	39de4a	TVF46CN	France	2025-05-23 05:22:24	2025-05-23 05:22:25	-2.8372	45.7037	11894.82	False	244.08	221.92	0.00	None	12047.22	7647	False	0
4	39de4c	TVF30BT	France	2025-05-23 05:22:25	2025-05-23 05:22:25	1.7482	46.7946	11277.60	False	239.05	192.30	0.00	None	11353.80	7653	False	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
5942	aa781f	CKS220	United States	2025-05-23 05:22:22	2025-05-23 05:22:22	-152.8475	64.3178	10972.80	False	249.00	258.92	0.00	None	10805.16	5246	True	0
5943	a4359b	UPS921	United States	2025-05-23 05:22:24	2025-05-23 05:22:24	-92.9572	38.3046	10675.62	False	273.22	88.92	0.00	None	10949.94	7252	False	0
5944	a0c7b3	ABX3114	United States	2025-05-23 05:22:25	2025-05-23 05:22:25	-81.9548	42.5158	11277.60	False	255.05	85.14	-0.33	None	11224.26	6264	False	0
5945	458664	CAT323	Denmark	2025-05-23 05:22:24	2025-05-23 05:22:25	10.7519	52.8907	11887.20	False	241.10	162.11	0.00	None	11757.66	3510	False	0
5946	458666	VKG1604	Denmark	2025-05-23 05:22:25	2025-05-23 05:22:25	10.6386	59.9969	5128.26	False	180.01	187.39	1.95	None	4945.38	4515	False	0

5947 rows × 17 columns